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High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium
Automated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958735/ https://www.ncbi.nlm.nih.gov/pubmed/27499923 http://dx.doi.org/10.1002/cjp2.42 |
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author | Abubakar, Mustapha Howat, William J Daley, Frances Zabaglo, Lila McDuffus, Leigh‐Anne Blows, Fiona Coulson, Penny Raza Ali, H Benitez, Javier Milne, Roger Brenner, Herman Stegmaier, Christa Mannermaa, Arto Chang‐Claude, Jenny Rudolph, Anja Sinn, Peter Couch, Fergus J Tollenaar, Rob A.E.M. Devilee, Peter Figueroa, Jonine Sherman, Mark E Lissowska, Jolanta Hewitt, Stephen Eccles, Diana Hooning, Maartje J Hollestelle, Antoinette WM Martens, John HM van Deurzen, Carolien Investigators, kConFab Bolla, Manjeet K Wang, Qin Jones, Michael Schoemaker, Minouk Broeks, Annegien van Leeuwen, Flora E Van't Veer, Laura Swerdlow, Anthony J Orr, Nick Dowsett, Mitch Easton, Douglas Schmidt, Marjanka K Pharoah, Paul D Garcia‐Closas, Montserrat |
author_facet | Abubakar, Mustapha Howat, William J Daley, Frances Zabaglo, Lila McDuffus, Leigh‐Anne Blows, Fiona Coulson, Penny Raza Ali, H Benitez, Javier Milne, Roger Brenner, Herman Stegmaier, Christa Mannermaa, Arto Chang‐Claude, Jenny Rudolph, Anja Sinn, Peter Couch, Fergus J Tollenaar, Rob A.E.M. Devilee, Peter Figueroa, Jonine Sherman, Mark E Lissowska, Jolanta Hewitt, Stephen Eccles, Diana Hooning, Maartje J Hollestelle, Antoinette WM Martens, John HM van Deurzen, Carolien Investigators, kConFab Bolla, Manjeet K Wang, Qin Jones, Michael Schoemaker, Minouk Broeks, Annegien van Leeuwen, Flora E Van't Veer, Laura Swerdlow, Anthony J Orr, Nick Dowsett, Mitch Easton, Douglas Schmidt, Marjanka K Pharoah, Paul D Garcia‐Closas, Montserrat |
author_sort | Abubakar, Mustapha |
collection | PubMed |
description | Automated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p‐value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p‐value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre‐ and post‐analytical quality control procedures are necessary in order to ensure satisfactory performance. |
format | Online Article Text |
id | pubmed-4958735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49587352016-08-05 High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium Abubakar, Mustapha Howat, William J Daley, Frances Zabaglo, Lila McDuffus, Leigh‐Anne Blows, Fiona Coulson, Penny Raza Ali, H Benitez, Javier Milne, Roger Brenner, Herman Stegmaier, Christa Mannermaa, Arto Chang‐Claude, Jenny Rudolph, Anja Sinn, Peter Couch, Fergus J Tollenaar, Rob A.E.M. Devilee, Peter Figueroa, Jonine Sherman, Mark E Lissowska, Jolanta Hewitt, Stephen Eccles, Diana Hooning, Maartje J Hollestelle, Antoinette WM Martens, John HM van Deurzen, Carolien Investigators, kConFab Bolla, Manjeet K Wang, Qin Jones, Michael Schoemaker, Minouk Broeks, Annegien van Leeuwen, Flora E Van't Veer, Laura Swerdlow, Anthony J Orr, Nick Dowsett, Mitch Easton, Douglas Schmidt, Marjanka K Pharoah, Paul D Garcia‐Closas, Montserrat J Pathol Clin Res Original Articles Automated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p‐value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p‐value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre‐ and post‐analytical quality control procedures are necessary in order to ensure satisfactory performance. John Wiley and Sons Inc. 2016-04-06 /pmc/articles/PMC4958735/ /pubmed/27499923 http://dx.doi.org/10.1002/cjp2.42 Text en © 2016 The Authors The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Abubakar, Mustapha Howat, William J Daley, Frances Zabaglo, Lila McDuffus, Leigh‐Anne Blows, Fiona Coulson, Penny Raza Ali, H Benitez, Javier Milne, Roger Brenner, Herman Stegmaier, Christa Mannermaa, Arto Chang‐Claude, Jenny Rudolph, Anja Sinn, Peter Couch, Fergus J Tollenaar, Rob A.E.M. Devilee, Peter Figueroa, Jonine Sherman, Mark E Lissowska, Jolanta Hewitt, Stephen Eccles, Diana Hooning, Maartje J Hollestelle, Antoinette WM Martens, John HM van Deurzen, Carolien Investigators, kConFab Bolla, Manjeet K Wang, Qin Jones, Michael Schoemaker, Minouk Broeks, Annegien van Leeuwen, Flora E Van't Veer, Laura Swerdlow, Anthony J Orr, Nick Dowsett, Mitch Easton, Douglas Schmidt, Marjanka K Pharoah, Paul D Garcia‐Closas, Montserrat High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title | High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title_full | High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title_fullStr | High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title_full_unstemmed | High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title_short | High‐throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium |
title_sort | high‐throughput automated scoring of ki67 in breast cancer tissue microarrays from the breast cancer association consortium |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958735/ https://www.ncbi.nlm.nih.gov/pubmed/27499923 http://dx.doi.org/10.1002/cjp2.42 |
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